Method for forming personal nutrition complex according to incidence of disease and genetic polymorphism by a prediction system

ABSTRACT

The present invention relates to a system for predicting an incidence of disease from genetic polymorphism and uses the prediction result to form a personal nutrition complex. The system collects at least one personal information and single nucleotide polymorphism (SNP) information then exchanges the above information with databases including a personal database, a genetic risk database, an allelic frequency database, and a prevalence database. Finally, the system will output a prediction report and indicates a risk of specific disease and a plurality of abnormal genes. According to the prediction results, the system also can provide a plurality of nutritional supplement ingredients to form a personal nutrition complex. Users can receive a comprehensive and an effective nutritional supplement countermeasure about abnormal genes for prevention of the specific disease.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a system for predicting an incidence ofdisease from genetic polymorphism and adopting results thereof to form apersonal nutrition complex.

2. Description of the Related Art

According to medical research, many diseases such as hyperglycemia,hyperlipidemia and hypertension, are related to a genetic polymorphism.The genetic polymorphism is normally attributed to genetic variationcaused by a nucleotide polymorphism (SNP), meaning that a singlenucleotide of a DNA sequence differs between alleles from differentgenotypes of biological species by substitution, insertion and deletion.As researches on SNP have been widely conducted in the medical field, itis known that a SNP can affect on protein function, gene expression orphysiologic reaction, and further affect on incidence of diseases orreactions and metabolic activities of medicine.

The lipoprotein lipase (LPL) gene is related to hypertension, elevatedplasma triglyceride and metabolic syndrome. Examination of LPL genesequence can be used to estimate the risk of suffering from the abovediseases of each individual.

In addition, the theory about the interaction between health, diet andgenes is provided with the advancement of nutrigenomics. This theorymaintains that balance or imbalance of the nutrition of intake willinfluence health and incidence of disease. According to the aboveresearch, many people start to eat nutritional components for thebenefit of their health. However, currently the nutritional supplementsavailable on the market are mostly composed by the regular ingredientswithout providing personalized nutrition complex for each individual. Sonow if someone needs to take multiple nutrition components, he or sheshould take a plurality of dosages at the same time, which is veryinconvenient.

SUMMARY OF THE INVENTION

An objective of the present invention is to provide a prediction systemfor indicating the incidence of the diseases and the abnormal genes forforming a personal nutrition complex. This system alerts subjects forearly prevention of disease. Furthermore, the system provides anindividual subject with a dietary recipe for a personal nutritioncomplex specifically designed based on genetic abnormality.

To achieve the foregoing objective, the system for predicting anincidence of a disease by a genetic polymorphism comprises:

a prediction server, the prediction server collecting at least onepersonal information and at least one genetic information for aninformation exchange process and a mathematical operation, and producinga prediction report for a user subsequently;

a personal database, the personal database connected with the predictionserver for receiving and storing the personal information;

a genetic risk database connected with the prediction server; thegenetic risk database including multiple SNP (single nucleotidepolymorphism) data and risk data that are correlated with the abovegenetic information;

an allelic frequency database connected with the prediction server; theallelic frequency database including a plurality of frequency datacorrelated with the SNP data and the risk data; and

a prevalence database connected with the prediction server; theprevalence database including a plurality of prevalence data for beingprovided to the server for the mathematical operation to produce theprediction report.

The advantage of the present invention is obtaining a prediction reportimmediately after testing. The prediction server collects a personalinformation and a genetic information to pass to the personal databasefor storage and the genetic risk database for information exchange.According to the genetic information, the SNP data and risk data areobtained from the genetic risk database. And then deliver the above SNPand the risk data to the allelic frequency database to exchange relatedfrequency data and obtain a prevalence data about testing from theprevalence database. After the above exchange information process, theprediction server receives the SNP data, the risk data, the frequencydata, and produce a prediction of genetic risk by utilizing the abovedata for a mathematical operation. Based on the genetic risk and theabove prevalence data, the system outputs a prediction report about thetesting. It is convenient, quick and efficient to obtain the predictionreport about the incidence of the disease and the mutation of the gene.This system can alert subjects for early prevention of disease.

The present invention is to further provide a method for forming apersonal nutrition complex according to incidence of disease and geneticpolymorphism by a prediction system comprising the steps of:

providing a biological sample taken from a subject;

testing SNP of a plural of genes in said sample and obtaining a result;

utilizing the prediction system to select nutritional supplementingredients according to the result; and

mixing the above nutritional supplement ingredients and forming apersonal nutrition complex.

According to the present invention, the personal nutrition complexconsists of a plural of ingredients. The number of ingredient is lessthan the number of the variation gene. It is effective to reducefrequency, mass and volume of taking nutritional supplement ingredientsfor the subjects.

Northern blotting or Southern blotting is utilized to test SNPs of theallelic genotype for different subjects or cells. The principle is usinga labeled nucleotide probe to hybridize with a filter membrane whichcomprises a target RNA or a DNA separated by electrophoresis andtransferred to the filter membrane. By this way, the target RNA or theDNA can be detected by the labeled nucleotide probe. Besides,examination of SNPs can also be conducted by amplifying a sequence of aspecific region of a target gene by a polymerase chain reaction (PCR)and then double checking the sequence accuracy by a DNA sequencing.Other skills about analyzing the sequence of SNPs sites such as, but notlimited to, a Ligase Chain Reaction (LCR), also can assist with a SNPgenotyping.

In order to discriminate SNPs of the sample, two labeled nucleotideprobes that are designed to have a SNP site of a specific gene can beutilized to test the sample. We can determine the SNP of the specificgene in the sample by observing whether a labeled nucleotide sample isbinding with the two labeled nucleotide probes or not. This method isutilizing the principle that two complementary nucleotides can bindtogether. Above of the two labeled nucleotide probes only contain adifference in a single nucleotide.

Preferably, the plural of genes include a adipogenesis-related gene, aappetite control gene, a metabolism gene and a endocrine regulationgene.

Preferably, by inputting a genetic testing result of the SNPs into theprediction system, incidence of a specific disease and an abnormal genecan be obtained. Then the prediction system will select a nutritionalsupplement ingredient that is related to the abnormal gene and form apersonal nutrition complex. When the genetic testing result indicatesthat the subject is susceptible to ectopic fat deposition and theadipogenesis-related gene is abnormal, the system will select firstnutritional supplement ingredients to form a personal nutrition complex.When the result indicates that the subject is susceptible to loss ofappetite control and the appetite control gene is abnormal, the systemwill select second nutritional supplement ingredients to form a personalnutrition complex. When the result indicates that the subject issusceptible to metabolic disorder and the metabolism gene is abnormal,the system will select third nutritional supplement ingredients to forma personal nutrition complex. When the result demonstrates that thesubject is susceptible to endocrine dysregulation and the endocrineregulation gene is abnormal, the system will select fourth nutritionalsupplement ingredients to form a personal nutrition complex.

According to the present invention, the first, second, third, and fourthnutritional supplement ingredients include plant extracts and syntheticcompounds. The plant extracts and the synthetic compounds are commonlyknown to be related to the plural genes of testing.

The adipogenesis-related gene is related to the fat deposition and thedifferentiation of the fat cell. The adipogenesis-related gene includes,but is not limited to, peroxisome proliferator-activated receptor gamma2 (PPARG2). The PPARG2 mainly involves in preadipocyte to adipocytedifferentiation. At the initial phase of adipocyte differentiation,C/EBPβ and C/EBPδ are induced first, and stimulated expression ofdownstream genes, C/EBPα and PPARγ2. The C/EBPβ and C/EBPδ genes playimportant roles in adipocyte differentiation and they can interact witheach other. When PPARG2 is activated, the downstream genes will beexpressed, and increased production of fat cells. A guanine nucleotidebinding protein beta-subunit 3 (GNB3) gene is in charge of producingBeta-3 subunit of a G-protein. The G-protein belongs to a signaltransduction protein on a cell membrane. It is involved in transmittingsignals from a variety of different pathway outside the cell intonucleus. The transmitting signals include MAPK signaling pathway inadipocyte differentiation.

The appetite control gene is related to controlling the sense ofsatisfaction, stress relaxation and appetite, including, but not limitedto, syndecan 3 (SDC3). SDC3 is a transmembrane protein. Expression ofthe SDC3 is upregulated in the brain hypothalamus of the feeding centerwhen fasting. The SDC3 will bind with AGRP and MC4R and form a complex,so that the appetite of the subject will be raised. Leptin (LEP) canmaintain the body fat percentage by controlling the appetite andincrease consumption of the energy. Melanocortin 4 receptor (MC4R) isrelated to the appetite and an energy exhaustion in a brain. TheMC4Rregulates function of food intake. MC4R defects can lead tooverweight and chronic hyperingestion.

The metabolism gene is related to metabolism of carbohydrate and lipids,including, but not limited to, uncoupling protein 3 (UCP3). The UCP3facilitates to transfer anions from an inner member to an outer membraneand reduce the mitochondrial membrane potential. The UCP3 gene isprimarily expressed in a skeletal muscle. Gene expression level of UCP3is increased with intake of fatty acid and glucose, and the body willproduce more energy. The other gene is beta-2-adrenergic receptor(ADRB2). The ADRB2 is related to a fight-or-flight response. People willreduce response of epinephrine if the ADRB2 gene is mutated. The ADRB2gene also can decrease the efficiency of glucose metabolism and affectcontractility of skeletal muscle and cardiac muscle. Peroxisomeproliferator-activated receptor-gamma coactivator 1, beta (PPARGC1B) canregulate transcription factors and nuclear receptors. The nuclearreceptors include estrogen receptors and glucocorticoid receptors thatcan affect metabolism of lipids, anaerobic glycolysis and energyexpenditure. Fat mass and obesity associated gene (FTO) can inhibit ametabolic rate and lead to slow motion. It also can inhibit metabolicenergy converted into heat within the body. The FTO deficient miceincrease in basal metabolic rate compared with normal mice.

The endocrine regulation gene is related to endocrine regulation anddirectly or indirectly affects energy expenditure and body fatdistribution, including, but not limited to, peroxisomeproliferator-activated receptor-gamma (PPARG). The structure of PPARG issimilar to the steroid and thyroid hormone receptor superfamily, calledperoxisome proliferators-activated receptor because PPARG can beswitched on by a peroxisome proliferating agents such as cloridrate,Nafenopin and WY14643. Estrogen receptor 1 (ESR1) can mediatemembrane-initiated estrogen signaling and indirectly influence energyexpenditure and body fat distribution. Nuclear receptor subfamily 0,group B, member 2 (NR0B2) is primarily expressed in the liver and usedto balance cholesterol and control the transcriptional activity forsecretion of insulin in the pancreas cell. If the NR0B2 is inactivated,the subject will be overweight.

In the present invention, the first nutritional supplement ingredientscan break down fat quickly and therefore avoid fat accumulation. Thefirst nutritional supplement ingredients includes, but not limited to,bitter orange (Citrus aurantium) flavonoids or roselle extracts. Thesecond nutritional supplement ingredients can control satiety, foodintake and stress release. The second nutritional supplement ingredientsinclude, but not limited to, banana peel extracts, vitamin B6, orvitamin B12. The third nutritional supplement ingredients can improvethe body's efficiency in using macronutrients (fat, carboxyhydrate andprotein). The third nutritional supplement ingredients include, but notlimited to lotus leave extracts, white kidney bean extracts, fermentedvegetable and fruit, and tea flower (Camellia sinensis) extracts. Thefourth nutritional supplement ingredients can stimulate or suppresshormone secretions. The fourth nutritional supplement ingredientsinclude, but not limited to, cranberry extracts or green tea extracts.

Compared to a normal allelic genotype, an abnormality indicates anallelic variant in the present invention. For example, functionalvariations of proteins or enzymes caused by the SNPs will lead tophysiological changes followed by enhancing risk of suffering specificdisease. If the SNP site in rs1822825 (G/A) of the PPARG is A, but notG, this result demonstrates that the PPARG gene is the geneticvariation. When SNP sites of two alleles are both A, the body is moreprone to obesity than when the SNP site of at least one allele is G.

According to the prediction system and method, the subject can receive aprediction result of disease and a plurality of abnormal genes by SNPgenotyping. The system further can select a plurality of nutritionalsupplement ingredients corresponding to the abnormal genes, but not justprovide only one nutritional supplement ingredient from a single gene.So the subject can receive a comprehensive and effective nutritionalsupplement countermeasure according to the plurality of abnormal genesby the prediction system.

Furthermore, the present method can mix the plurality of nutritionalsupplement ingredients to form a complex corresponding to the abnormalgenes that are selected from the prediction system. The present methodhas advantages over the prior arts that disperse many nutritionalsupplement ingredients to multiple tablets. This method can effectivelycontrol volume and number of tablets and also provides a personalnutritional complex for each individual and draft a standard dosage. Thepresent method can encourage people to take nutritional supplementcomplex and reduce numbers of tablets and mistakes of frequency.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of the prediction system for incidence ofdisease by genetic polymorphism in accordance with the presentinvention;

FIG. 2 is an application mode diagram of the prediction system forincidence of disease by genetic polymorphism in accordance with thepresent invention;

FIG. 3 is a statistics curve chart for prediction report of theprediction system for incidence of disease by genetic polymorphism inaccordance with the present invention;

FIG. 4 is another application mode diagram of the prediction system forincidence of disease by genetic polymorphism in accordance with thepresent invention;

FIG. 5 is another statistics curve chart for prediction report of theprediction system for incidence of disease by genetic polymorphism inaccordance with the present invention;

FIG. 6 is still another application mode diagram of the predictionsystem for incidence of disease by genetic polymorphism in accordancewith the present invention;

FIG. 7 is still another statistics curve chart for prediction report ofthe prediction system for incidence of disease by genetic polymorphismin accordance with the present invention; and

DETAILED DESCRIPTION OF THE INVENTION

With reference to FIG. 1, the prediction system for incidence of diseaseby genetic polymorphism comprises a prediction server 10, a personaldatabase 20, a genetic risk database 30, an allelic frequency database40, and a prevalence database 50.

The prediction server 10 is connected with at least one user terminal60. The prediction server 10 is also connected with the personaldatabase 20, the genetic risk database 30, the allelic frequencydatabase 40, and the prevalence database 50. A user can input at leastone personal information and at least one genetic information to theuser terminal 60 and then the prediction server 10 will exchangeinformation to the personal database 20. After information exchangeprocess, the prediction system will go on a mathematical operation andthen produces a prediction report. By this way, user can receive theprediction report from a report output terminal 70 shortly.

The personal database 20 is used to receive the personal informationfrom the prediction server 10 and store the received personalinformation. The personal database 20 also can provide saved personalinformation for the prediction server 10 to read at any time.

The genetic risk database 30 is used to receive a genetic informationfrom the prediction server 10. The genetic risk database 30 has many SNPdata and risk data corresponding to the genetic information. Accordingto the genetic information, the prediction server 10 exchangesinformation with the genetic risk database 30 and obtains acorresponding SNP data and risk data.

In one embodiment, the genetic risk database 30 further includes a SNParea 31 and a risk area 32. The SNP area 31 is used to store and readthe SNP data. The SNP data includes a plurality of genotypes. Eachgenotype is composed of a pair of alleles, one from the father, and theother from mother. For example, when the alleles are G and A in the SNPdata, the genotype may comprise three forms of GG, GA or AA.

The risk area 32 is used to store and read the risk data. The risk datais an Odds Ratio (OR) data. The OR data is calculated from the odds bytwo things. In one embodiment, the OR data implies the genetic orallelic risk of disease.

The allelic frequency database 40 is used to receive and store the SNPdata and the risk data from the prediction server 10. The allelicfrequency database 40 has a plurality of frequency data corresponding tothe SNP data and risk data. The prediction server 10 obtains a frequencydata after exchanging data with the allelic frequency database 40. Inone embodiment, the frequency data is an allelic data of frequency,which is a ratio between alleles and genotypes in a group. For example,the frequency is 0.5 when three among six people have GG genotype. Thefrequency is 0.333 when two among six people have GA genotype. Thefrequency is 0.167 if only one among six people has AA genotype. Whenthe number of allele is twelve, for eight of twelve alleles being G, theallelic frequency is 0.667. For four of the twelve alleles being A, theallelic frequency is 0.333.

The prevalence database 50 has a multiple prevalence data. Theprediction server 10 obtains a prevalence data that is related to thetest subject from the prevalence database 50. After the predictionserver 10 obtains the SNP data, the risk data and the frequency data bydata exchange process and calculate a plural of relative risks (RR), theprediction system can output a genetic risk. The prediction system alsocan output a prediction report quickly about the test subject accordingto the relative risk and the prevalence data.

In one embodiment, the genetic risk database 30, the allelic frequencydatabase 40 and the prevalence database 50 are external databases. Theprediction server 10 connects to the external databases and obtains thelatest SNP data, risk data, frequency data and prevalence data from theexternal databases at any time.

In one embodiment, the prediction server 10 collects a personalinformation and a genetic information related to the test subjectthrough the user terminal 60. The prediction server 10 passes the aboveinformation to the personal database 20 and the genetic risk database 30to exchange information. According to the genetic information, theprediction system can obtain SNP data and OR data from the genetic riskdatabase 30. Subsequently, the prediction system passes the SNP data andthe OR data to the allelic frequency database 40 to exchange data. Thenthe prediction system can further obtain a corresponding frequency data.Through the prevalence database 50, the user can obtain a prevalencedata about the test subject.

When the prediction server 10 obtains the SNP data, the OR data and thefrequency data by the above information exchange process and calculatesthe relative risk (RR), the prediction server 10 further produces agenetic risk by the relative risk (RR). Through calculating the geneticrisk and the above prevalence data, user can quickly get a predictionreport for every physiological stage. User can use this convenient, fastand efficient method to receive a reference about incidence of diseasefor their genes for early prevention of diseases.

With reference to FIG. 2, a human subject had been tested for diabetesmellitus type II in a hospital. The hospital can obtain a personalinformation (citizenship, age and credentials) and a geneticinformation. The human subject or medical staff can connect theprediction server 10 and the user terminal 60. Then the human subject ormedical staff can use a credential to sign in the prediction system.Finally, the human subject or medical staff can obtain a predictionreport from the report output terminal 70. The prediction reportincludes the following information.

The SNP data related to Diabetes mellitus type II comprises multiplegenes and the SNP sites of the multiple genes which includes thers13266634 of SLC30A8 gene, the rs2237895 of KCNQ1 gene, the rs17584499of PTPRD gene, the rs391300 of SRR gene, the rs5219 of KCNJ11 gene, thers10946398 of CDKAL1 gene, the rs10811661 of CDKN2A/B gene, thers7903146 of TCF7L2 gene, the rs1111875 of HHEX gene and the rs1801282of PPARG gene. The SNP data is corresponding to a plurality of gene data(genotype) and relative risk (RR). It further provides the genetic riskto the subject.

The prevalence database 50 provides a plurality of prevalence data. Foran average of incidence of Chinese, the prevalence data is related toDiabetes mellitus type II of all ages. The prediction incidence ofDiabetes mellitus type II is produced by the prevalence data and thegenetic risk of all ages for subjects.

With reference to FIG. 3, the curve chart is an analysis result of anincidence of Diabetes mellitus type II. The horizontal axis representsages, and the vertical axis represents percentage of incidence. The agein horizontal axis is from 20 to 79 with each stage being ten years.When Chinese' percentage of incidence from aged 40 to 59 years old risesfrom 5.7% to 14.3%, the human subject's percentage of incidence risesfrom 3.75% to 9.41%, which is lower than the average of incidence ofChinese, showing that the human subject is healthier. However, the humansubject also has a similar rising trend from aged 40 to 59 years oldwith the rising trend of the Chinese. So the human subject has to takecare about his diet and lifestyle to prevent Diabetes mellitus type II.

With reference to FIG. 4, one Chinese subject had been tested forhypertension in a hospital. The subject or medical staff can obtain aprediction report from the report output terminal 70. The predictionreport included the following information:

The SNP data related to hypertension comprises multiple genes and theSNP sites of the multiple genes which includes the rs699 of AGT gene,the rs4961 of ADD1 gene, the rs1799983 of NOS3 gene, the rs11191548 ofCYP17A1 gene, the rs16998073 of FGF5 gene, the rs5186 of AGTR1 gene, thers3865418 of NEDD4L gene, the rs3754777 of STK39 gene and the rs3781719of CALCA gene. Each gene is corresponding to the plurality of gene data(genotype) and relative risk (RR) for the subject. It also provides agenetic risk to the subject. The prevalence database 50 provides aplurality of prevalence data. For an average of incidence of Chinese,the prevalence data is relate to hypertension of all ages. Theprediction incidence of hypertension is produced by the prevalence dataand the genetic risk of all ages for subject.

With reference to FIG. 5, the curve chart is an analysis result aboutincidence of hypertension. The horizontal axis represents ages, and thevertical axis represents percentage of incidence. The age in horizontalaxis is from 20 to 79 and each stage is ten years old. When the Chinese'percentage of incidence from aged 20 to 39 years old rises from 3.7% to11.9%, the subject's percentage of incidence rises from 3.49% to 11.21%.The percentage of incidence the subject is identical to percentage ofincidence of the Chinese when his age is from 20 to 39. Even when thesubject's age is from 70 to 79, the percentage of incidence is similarto the Chinese. So the subject has to take care about his diet andlifestyle more carefully to prevent the hypertension.

FIG. 6, this is another application mode identical to the aboveembodiments. The only difference is the test subject. The test subjectis related to hyperlipidemia. The human subject or medical staff canobtain a prediction report from the report output terminal 70. Theprediction report includes the following information.

The SNP data related to hyperlipidemia comprises multiple genes and theSNP sites of the multiple genes which includes the rs1003723 of LDLRgene, the rs1367117 of APOB gene, the rs2075291 of APOA5 gene, the rs326of LPL gene, the rs4420638 of APOE gene, the rs780094 of GCKR gene, thers4846914 of GALNT2 gene, the rs1800588 of LIPC gene, the rs12654264 ofHMGCR, the rs3764261 of CETP gene, and the rs17145738 of MLXIPL gene.These genes are related to the plurality of gene data (genotype) andrelative risk (RR) for the test subject. They also provide a geneticrisk to the test subject. The prevalence database 50 provides aplurality of prevalence data. For an average of incidence of Chinese,the prevalence data is related to hyperlipidemia of all ages. Theprediction incidence of hyperlipidemia is produced by the prevalencedata and the genetic risk of all ages for the subject.

With reference to FIG. 7, the curve chart is an analysis result relatedto incidence of hyperlipidemia. The horizontal axis represent ages, andthe vertical axis represents percentage of incidence. The age inhorizontal axis is from 20 to 79 and each stage is ten years old. Whenthe Chinese' percentage of incidence from aged 40 to 59 years old risesfrom 19.7% to 28.6%, the human subject's percentage of incidence risesfrom 9.59% to 13.93%. So the percentage of incidence of the humansubject is lower than the percentage of incidence of the Chinese,indicating that the human subject is healthier. However, the humansubject also has to take care about his lifestyle.

From the foregoing, the prediction system of the present invention forincidence of disease by genetic polymorphism mainly collects personalinformation and genetic information by the prediction server 10. Theprediction server 10 transfers the personal information to the personaldatabase 20 for storage, and exchange the personal information with thegenetic risk database 30. According to the SNP data and the risk data,the prediction system transfers the above SNP data and risk data to theallelic frequency database 40 to exchange a relative frequencyinformation and obtain a prevalence information from the prevalencedatabase 50. After the above exchange process, the prediction server 10receives a SNP data, a risk data, a frequency data, and produces agenetic risk by above data. Based on the genetic risk and the aboveprevalence information, the system outputs a prediction report. It is aconvenient, quick and efficient method to obtain a prediction reportabout the incidence of the disease and the mutation of the gene. Thissystem can alert subjects for early prevention of disease.

This invention uses known SNPs of genes to recognize the specific SNPsite and genotype of adipogenesis-related gene, appetite control gene,metabolism gene and endocrine regulation gene by the biological samplefrom human subject. The prediction system of an incidence of diseasewill determine an incidence of disease and an abnormal gene by enteringthe genotype to the system. It means human subject is susceptible tospecific disease. Then the system will select nutritional supplementingredients according to the abnormal gene and mix the ingredients witha carrier to form a nutritional complex tablet. In the followingembodiments, the system determines more than 500 genotypes that havemiddle and high risk to suffer disease. According to the embodiments, apersonal nutrition complex can be formed in advance to fight disease.Furthermore, the system can provide many kinds of compositions tocomplete the prevention for different human subjects.

In a preferred embodiment, the gene of adipogenesis is peroxisomeproliferator-activated receptor gamma 2 (PPARG2) or guanine nucleotidebinding protein beta-subunit 3 (GNB3).

In a preferred embodiment, the gene of appetite control is syndecan 3(SDC3), leptin (LEP) or melanocortin 4 receptor (MC4R).

In a preferred embodiment, the SNP sites of genes includePPARG-rs1822825 (G/A), PPARGC1B-rs7732671 (G/C), PPARG2-rs1801282 (C/G),GNB3-rs5443 (C/T), LEP-rs104894023 (C/T), SDC3-rs2282440 (C/T),MC4R-rs121913561(A/G), UCP3-rs17848368 (C/T), ADRB2-rs1042714 (C/G),NR0B2-rs74315350 (G/T), APOE-rs429358 (T/C), GHRL-rs696217 (C/A),FTO-rs6499640 (A/G), ESR1-rs712221 (A/T) and AGT-rs699 (T/C). Personshaving ordinary skills in the art can choose proper SNP sites accordingto the corresponding strategy and four gene types.

In a preferred embodiment, the SNP sites of gene are the rs1801282 ofPPARG2 gene, the rs5443 of GNB3 gene, the rs2282440 of SDC3 gene, thers104894023 of LEP gene, the rs121913561 of MC4R gene, the rs17848368 ofUCP3 gene, the rs1042714 of ADRB2 gene, the rs7732671 of PPARGC1B gene,the rs6499640 of FTO gene, the rs1822825 of PPARG gene, the rs74315350of NR0B2 gene and the rs712221 of ESR1 gene.

In a preferred embodiment, the first nutritional supplement ingredientis bitter orange (Citrus aurantium) flavonoids or roselle extracts.

In a preferred embodiment, the second nutritional supplement ingredientis banana peels extracts, vitamin B6 or vitamin B12.

In a preferred embodiment, the third nutritional supplement ingredientis lotus leave extracts, white kidney bean extracts, fermented vegetableand fruit, or tea flower (Camellia sinensis) extracts.

In a preferred embodiment, the fourth nutritional supplement ingredientis cranberry extracts or green tea extracts.

According to the present invention, the extract is crushed and grindedfrom the material and then mixed with an aqueous solvent or a non-polarreagent. Then the extract is produced by a freeze-dried step afterfiltering. For example, the lotus leave extracts is dried, crushed andgrinded from the lotus then mix with the aqueous solvent. Finally thepowder of lotus leave extracts is produced by freeze-dried step afterfiltering.

In a preferred embodiment, the carrier includes, but is not limited to,excipients, diluents, disintegrants, glidants, binders, lubricants,anti-adhesion agent and/or glidants. Furthermore, sweeteners, flavors,coloring agents and/or coating can be added to achieve a specificpurpose.

In a preferred embodiment, the number of carrier is in accordance withoral dose. The oral dose means user does not have difficulty swallowingthat is declared in the pharmacopoeia clearly. The solid reagent is apastille, a tablet or a capsule. The diameter of the solid reagent isless than 1.5 cm. The weight of solid reagent is less than 1.5 g. Thenumber of solid reagent is less than 15, preferably 12, more preferablyis 10 to 5, and further more preferably is 1. Specifically, the solidreagent is a spherical pastille and the weight of each sphericalpastille is 0.7 g. The solid reagent is a powder or a granules. Thetotal weight of the solid reagent is less than 20 g, preferably 10 g,and more preferably is 8.4 g.

Even though numerous characteristics and advantages of the presentinvention have been set forth in the following description, togetherwith details of the field and technology of the invention, thedisclosure is illustrative only. Do not limit present invention of thescope.

EMBODIMENT

A DNA sample was obtained from a volunteer. The genotype of SNP wasdetermined by TaqMan (TaqMan® SNP Genotyping Assays, purchased fromApplied Biosystems Inc.). The assays utilized two probes of wild-typeand mutant-type in accordance with SNP to hybridize specifically to thedifferentiated allele. The probe 5′ is labeled with differentfluorescents, which are called reporter dye. The reporter dye usually isa FAM™ dye and a VIC™ dye and can also be replaced with other dyes suchas a TET dye. Then probe 3′ is labeled with a fluorescent absorber,which is called a quencher dye, and is a non-fluorescent. Thefluorescent absorber usually is tetramethylrhodamine (TAMRA). When thetwo probes has not yet hybridized with DNA templates, the quencher dyeon the probe 3′ can absorb energy of the fluorescent from the reporterdye on the probe 5′. With this mechanism, the fluorescent dye can'trelease fluorescent until polymerase chain reaction (PCR) is started.The DNA polymerase with 5′exonuclease function will cut off probes thatare attached to the DNA template. Then the reporter dye and the quencherdye are separated from the probes. Finally, the fluorescent dye on theprobe 5′ is excited and releases fluorescence which can be detected by afluorescent reader. The analysis for SNP of PPARG, PPARG2, PPARGC1B,GNB3, LEP, SDC3, MC4R, UCP3, ADRB2, NR0B2, FTO and ESR1 is achieved byusing TaqMan Assays.

The SNP sites of genes are the rs1801282 of PPARG2 gene, the rs5443 ofGNB3 gene, the rs104894023 of LEP gene, the rs2282440 of SDC3 gene, thers121913561 of MC4R gene, the rs17848368 of UCP3 gene, the rs1042714 ofADRB2 gene, the rs6499640 of FTO gene, the rs74315350 of NR0B2 gene, thers1822825 of PPARG gene and the rs712221 of ESR1 gene.

Table 1 shows analysis results of allele and nucleotide sequence for theabove SNP sites:

TABLE 1 Gene Low risk Middle risk High risk PPARG2 C/C C/G G/G GNB3 C/CC/T T/T LEP C/C C/T T/T SDC3 C/C C/T T/T MC4R A/A A/G G/G UCP3 T/T T/CC/C ADRB2 C/C C/G G/G PPARGC1B G/G G/C C/C FTO G/G G/A A/A NR0B2 G/G G/TT/T PPARG G/G G/A A/A ESR1 A/A A/T T/T

When the above genotype of gene belongs to the middle risk and high riskgroups, the prediction system will determine that the gene is anabnormal gene. The prediction system selects nutritional supplementingredients in accordance with the abnormal genes by discriminatinggenotype of SNP sites for PPARG2, GNB3, LEP, SDC3, MC4R, UCP3, ADRB2,PPARGC1B, FTO, NR0B2, ESR1, and PPARG gene. If PPARG2 and GNB3 areabnormal genes, the system will choose bitter orange (Citrus aurantium)flavonoids and roselle extracts to form a complex. If SDC3 is anabnormal gene, the system will choose banana peels extracts, vitamin B6and vitamin B12 to form a complex. If UCP3, ADRB2, PPARGC1B and FTO areabnormal genes, the system will choose lotus leave extracts, whitekidney bean extracts, fermented vegetable & fruit and tea flower(Camellia sinensis) extracts to form a complex. If ESR1 and PPARG areabnormal genes, the system will choose cranberry extracts and green teaextracts to form a complex.

Table 2 shows nutritional supplement ingredients correlated with thegenes as follows:

TABLE 2 Ingredients of Cate- Nutritional Embodiment gory Gene Supplement1 2 3 4 5 6 7 1 PPARG2 Bitter Orange Flavonoids (400 mg) GNB3 Roselle VV V V V V V Extracts (350 mg) 2 SDC3 Banana Peels V V V V V V V Extracts(100 mg) Vitamin B6 (1.5 mg) Vitamin B12 (2.4 μg) 3 UCP3 Lotus leave V VV Extracts (1.2 g) ADRB2 White Kidney V V V V V Bean Extracts (1.2 g)PPARGC1B Fermented V V V V V Vegetable & Fruit (500 mg) FTO tea flower VV V V V (Camellia sinensis) Extracts (200 mg) 4 ESR1 Cranberry V V V V VV V Extracts (100 mg) PPARG Green Tea V V V V V V V Extracts (450 mg) —— Add carrier to 8.4 g The “V” symbol is employed here to indicate theuse of nutritional supplements or carriers for those identified to havebeen related to the abnormal gene having middle or high risk genotype.Then related nutritional supplement ingredients and carriers areselected to form the personal nutrition supplement.

According to the test result, the prediction system will select and mixrelated nutritional supplement ingredients when the SNP sites ofGNB3-rs5443, SDC3-rs2282440, ADRB2-rs1042714, PPARGC1B-rs7732671,FTO-rs6499640, ESR1-rs712221, PPARG-rs1822825 are in the high risk. Thenutritional supplement ingredients include roselle extracts (40% rosellecalyx extract powder, COMPSON TRADING CO., LTD), banana peels extracts(50 mg/g Serontoinic freeze-dried powder, TCI Firstek CORP.), vitamin B6or vitamin B12, white kidney bean extracts (10000 unit/g PHY, TCIFirstek CORP.), fermented vegetable & fruit (TCI CO., LTD), tea flower(Camellia sinensis) extracts (Japanese HARIMA, Mitsubo Co., LTD),cranberry extracts (COMPSON TRADING CO., LTD) and green tea extracts(90% polyphenols IND/EGCG 46.4%, TCI Firstek CORP.). The extracts arecrushed and grinded from the material and then mixed with an aqueoussolvent or a non-polar reagent. Then the extracts are produced by afreeze-dried step after filtering. Then the personal nutrition complexin the embodiment 1 is made by a tableting technique. Similarly, theprediction system will select and mix roselle extracts, banana peelsextracts, vitamin B6 or vitamin B12, lotus leave extracts, fermentedvegetable & fruit, tea flower (Camellia sinensis) extracts, cranberryextracts, green tea extracts and predetermined amount of carrier whenthe SNP sites of GNB3, SDC3, UCP3, PPARGC1B, FTO, ESR1, PPARG areabnormal. Then the personal nutrition complex in the embodiment 2 ismade by a tableting technique. The following embodiments 3-7 use thesame way to form a personal nutrition complex. The personal nutritioncomplex in the above embodiments is made to 12 tablets, and can providea personal supplement method for more than 4 situations of genemutation. The method also can provide a regular number of dosage fordifferent user to prevent frequency mistake of intake.

According to the embodiments 1-7, the prediction system provides thepersonal nutrition complex to select volunteer for the human subject.Compared to the nutritional complex provided randomly, the presentinvention can effectively control generation and deposition of fat forweight maintenance.

According to the above embodiments, the present invention also can mixother nutritional supplement ingredients with high concentration andthen form less than 4 tablets to reduce numbers of formulations. Presentinvention allows user to eat nutritional supplement complexconveniently.

What is claimed is:
 1. A prediction system for an incidence of diseaseby genetic polymorphism comprising: a prediction server, the predictionserver collecting at least one personal information and at least onegenetic information for an information exchange process and amathematical operation, and producing a prediction report for a usersubsequently; a personal database, the personal database connected withthe prediction server for receiving and storing the personalinformation; a genetic risk database connected with the predictionserver; the genetic risk database including multiple SNP (singlenucleotide polymorphism) data and risk data that are correlated with theabove genetic information; an allelic frequency database connected withthe prediction server; the allelic frequency database including aplurality of frequency data correlated with the SNP data and the riskdata; and a prevalence database connected with the prediction server;the prevalence database including a plurality of prevalence data forbeing provided to the server for the mathematical operation to producethe prediction report.
 2. The system as claimed in claim 1, wherein thegenetic risk database includes a SNP area and a risk area; the SNP areais provided to read and store the SNP data and the SNP data includes aplurality of genotypes; the risk area is used to read and store the riskdata and the risk data is odds ratio.
 3. The system as claimed in claim2, wherein the frequency data is a frequency data of the allele.
 4. Thesystem as claimed in claim 3, wherein the frequency data of allele isthe ratio between alleles and genotypes in a group.
 5. The system asclaimed in claim 4, wherein the server obtains the SNP data, the riskdata and the frequency data for the information exchange process; thenthe system utilizes the SNP, the risk, and the frequency data tocalculate multiple relative risk values before a user gets a geneticrisk data based on each relative risk value.
 6. The system as claimed inclaim 5, wherein the system calculates the genetic risk data and theprevalence data to generate a prediction about incidence of disease. 7.The system as claimed in claim 6, wherein the SNP data includes thers13266634 of SLC30A8 gene, the rs2237895 of KCNQ1 gene, the rs17584499of PTPRD gene, the rs391300 of SRR gene, the rs5219 of KCNJ11 gene, thers10946398 of CDKAL1 gene, the rs10811661 of CDKN2A/B gene, thers7903146 of TCF7L2 gene, the rs1111875 of HHEX gene, and the rs1801282of PPARG gene.
 8. The system as claimed in claim 6, wherein the SNP dataincludes the rs699 of AGT gene, the rs4961 of ADD1 gene, the rs1799983of NOS3 gene, the rs11191548 of CYP17A1 gene, the rs16998073 of FGF5gene, the rs5186 of AGTR1 gene, the rs3865418 of NEDD4L gene, thers3754777 of STK39 gene, and the rs3781719 of CALCA gene.
 9. The systemas claimed in claim 6, wherein the SNP data includes the rs1003723 ofLDLR gene, the rs1367117 of APOB gene, the rs2075291 of APOA5 gene, thers326 of LPL gene, the rs4420638 of APOE gene, the rs780094 of GCKRgene, the rs4846914 of GALNT2 gene, the rs1800588 of LIPC, thers12654264 of HMGCR, the rs3764261 of CETP gene, and the rs17145738 ofMLXIPL gene.
 10. The system as claimed in claim 1, wherein the systemfurther provides at least one user terminal that is connected with theprediction server for inputting the personal information and the geneticinformation; the system produces the prediction report for the user bythe information exchange process and the mathematical operation andoutputs the prediction report through an output terminal.
 11. A methodfor forming personal nutrition complex according to an incidence ofdisease and genetic polymorphism by a prediction system comprising thesteps of: providing a biological sample taken from a subject; testingSNP of a plurality of genes in said sample and obtaining a result;utilizing the system of claim 1 to select nutritional supplementingredients according to the result; and mixing the nutritionalsupplement ingredients to form a personal nutrition complex.
 12. Themethod as claimed in claim 11, wherein the plurality of genes includethe gene of adipogenesis, the gene of appetite control, the gene ofmetabolism and the gene of endocrine regulation, and the nutritionalsupplement ingredients include first, second, third, and fourthnutritional supplement ingredients; when the result demonstratesabnormalities in the gene of adipogenesis, the first nutritionalsupplement ingredients are selected to form a personal nutritioncomplex; when the result demonstrates abnormalities in the gene ofappetite control, the second nutritional supplement ingredients areselected to form a personal nutrition complex; when the resultdemonstrates abnormalities in the gene of metabolism, the thirdnutritional supplement ingredients are selected to form a personalnutrition complex; when the result demonstrates abnormalities in thegene of endocrine regulation, the fourth nutritional supplementingredients are selected to form a personal nutrition complex.
 13. Themethod as claimed in claim 12, wherein the gene of adipogenesis isperoxisome proliferator-activated receptor gamma 2 (PPARG2) or guaninenucleotide binding protein beta-subunit 3 (GNB3), and the SNP site isrs1801282 of PPARG2 and rs5443 of GNB3.
 14. The method as claimed inclaim 12, wherein the gene of appetite control is syndecan 3 (SDC3),leptin (LEP) or melanocortin 4 receptor (MC4R), and the SNP site isrs2282440 of SDC3, rs104894023 of LEP, and rs121913561 of MC4R.
 15. Themethod as claimed in claim 12, wherein the gene of metabolism isuncoupling protein 3 (UCP3), beta-2-adrenergic receptor (ADRB2),peroxisome proliferator-activated receptor-gamma coactivator 1, beta(PPARGC1B), or fat mass and obesity associated gene (FTO), and the SNPsite is rs17848368 of UCP3, rs1042714 of ADRB2, and rs6499640 of FTO.16. The method as claimed in claim 12, wherein the gene of endocrineregulation is peroxisome proliferator-activated receptor-gamma (PPARG),nuclear receptor subfamily 0, group B, member 2 (NR0B2) or estrogenreceptor 1 (ESR1), and the SNP site is rs1822825 of PPARG, rs74315350 ofNR0B2, and rs712221 of ESR1.
 17. The method as claimed in claim 11,wherein the mixing step includes mixing nutritional supplementingredients with a carrier before forming the nutrition complex to atablet by tableting technology.
 18. The method as claimed in claim 11,wherein the personal nutrition complex is composed of multipleformulations; and the number of the multiple formulations is less thanthe number of genes.
 19. The method as claimed in claim 11, wherein theSNP sites are the rs1801282 of PPARG2 gene, the rs5443 of GNB3 gene, thers2282440 of SDC3 gene, the rs104894023 of LEP gene, the rs121913561 ofMC4R gene, the rs17848368 of UCP3 gene, the rs1042714 of ADRB2 gene, thers6499640 of FTO gene, the rs1822825 of PPARG gene, the rs74315350 ofNR0B2 gene and the rs712221 of ESR1 gene; and the nutritional supplementingredient is selected from bitter orange (Citrus aurantium) flavonoids,roselle extracts, and mixtures thereof; and banana peels extracts,vitamin B6, vitamin B12 and mixtures thereof; and lotus leave extracts,white kidney bean extracts, fermented vegetable and fruit, tea flower(Camellia sinensis) extracts and mixtures thereof; and cranberryextracts, green tea extracts and mixtures thereof.